How simple image tweaks — Data Augmentation — can boost AI accuracy
Big image AI needs lots of examples, but collecting them is slow and costly, so people copy and change pictures to make more training examples.
This study checked common ways to change images and found one clear winner: image cropping often gives the biggest jump in performance.
The team tested different color and shape changes on a modest image set, using a simple neural network, and the tests was repeated to be fair.
Cropping made the model see parts of images in new ways, so it learned to focus on different details; this helped the model guess right more often.
In short, smart image changes can cut the need for huge photo banks and speed up training.
If you work with pictures, try adding cropped versions of your shots, it might raise your accuracy more then other tricks.
These are easy steps, low cost, and useful for many projects that use data to teach computers to see.
Read article comprehensive review in Paperium.net:
Improving Deep Learning using Generic Data Augmentation
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